End of training

README.md

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+---
+library_name: transformers
+base_model: microsoft/codebert-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: vuln-patch-cwe-guesser-model-microsoft-codebert-base
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# vuln-patch-cwe-guesser-model-microsoft-codebert-base
+
+This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 4.2765
+- Accuracy: 0.2614
+- F1: 0.0104
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 3e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 1
+- label_smoothing_factor: 0.1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| 4.713         | 1.0   | 25   | 4.2765          | 0.2614   | 0.0104 |
+
+
+### Framework versions
+
+- Transformers 4.55.0
+- Pytorch 2.7.1+cu126
+- Datasets 4.0.0
+- Tokenizers 0.21.2

emissions.csv

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+timestamp,project_name,run_id,experiment_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,on_cloud,pue
+2025-08-11T09:41:07,codecarbon,c7946fe2-006d-49c1-87e3-8e64888f7f4f,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,12.5743139218539,0.00022532905383394798,1.791978912203955e-05,42.5,476.50199247942726,94.34468364715576,0.000148328986798232,0.0016630429970234673,0.00032925723854464,0.002140629222366339,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-60-generic-x86_64-with-glibc2.39,3.12.3,2.8.4,64,AMD EPYC 9124 16-Core Processor,2,2 x NVIDIA L40S,6.1294,49.6113,251.58582305908203,machine,N,1.0

metrics.json

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+{
+    "eval_loss": 4.276517868041992,
+    "eval_accuracy": 0.26136363636363635,
+    "eval_f1": 0.01036036036036036,
+    "eval_runtime": 0.4791,
+    "eval_samples_per_second": 183.688,
+    "eval_steps_per_second": 6.262,
+    "epoch": 1.0
+}